Getting started
Before starting
Before starting, you will need:
- a running Elasticsearch cluster (6.X)
- with an index (your indexed data) having an identifier, a timestamp, a geometry and a centroid
- a machine with Docker for running ARLAS-WUI and ARLAS-Server.
Steps
To setup the ARLAS Exploration Stack, you can either start the required docker containers or start individually the processes on the command line (one rest
server and one http
server).
Tip
For simplicity, we recommend using the docker containers for running the ARLAS Exploration Stack.
Steps
To setup the ARLAS Exploration Stack, you will need to
- configure & start ARLAS Server
- register your Elasticsearch index as an ARLAS Collection
- and finally configure & start ARLAS WUI
Using docker
Step 1: ARLAS Server
docker run -d \
-p 9999:9999 \
-e "ARLAS_ELASTIC_CLUSTER=myelasticsearchclustername" \
-e "ARLAS_ELASTIC_NODES=elasticsearchhostname:9300" \
gisaia/arlas-server:latest
Important
ARLAS_ELASTIC_HOST
and ARLAS_ELASTIC_PORT
no longer exist starting from v10.6.0. They're replaced by ARLAS_ELASTIC_NODES
.
For further configuration details, see the ARLAS Server configuration page.
Step 2: Registering data
Register your Elasticsearch index within ARLAS Exploration:
curl -X PUT \
--header 'Content-Type: application/json;charset=utf-8' \
--header 'Accept: application/json' \
'http://localhost:9999/arlas/collections/mycollection' \
--data @collection.json
where collection.json
contains:
{
"index_name": "my.index.name",
"type_name": "type.within.my.index",
"id_path": "path.to.the.id",
"geometry_path": "path.to.the.geometry",
"centroid_path": "path.to.the.centroid",
"timestamp_path": "path.to.the.timestamp",
"include_fields": "*"
}
See the tutorial and the collection api documentation for further help on how to manage your ARLAS Collections. Read the collection model documentation to understand the fields within your index required for becoming an ARLAS Collection.
Step 3: ARLAS WUI
Setup a configuration file (config.json
) for ARLAS-WUI and then start it by providing the HTTP URL of the configuration file:
docker run -d \
-p 80:80 \
-e ARLAS_WUI_CONFIGURATION_URL=http://myhostname/path/to/config/file.json \
-e ARLAS_WUI_MAP_CONFIGURATION_URL=http://myhostname/path/to/config/file.map.json \
-e ARLAS_WUI_ABOUT_CONFIGURATION_URL=http://myhostname/path/to/config/file.md\
-e ARLAS_WUI_I18N_EN_URL=http://myhostname/path/to/english/language/file_en.json \
-e ARLAS_WUI_I18N_FR_URL=http://myhostname/path/to/french/language/file_fr.json \
gisaia/arlas-wui:latest
Note
ARLAS_WUI_MAP_CONFIGURATION_URL
, ARLAS_WUI_ABOUT_CONFIGURATION_URL
, ARLAS_WUI_I18N_EN_URL
and ARLAS_WUI_I18N_FR_URL
are optional.
Now, ARLAS WUI is up and running.
Optional : ARLAS-tagger
You can use ARLAS-tagger for building sophisticated data sets.
you will need:
- a running Elasticsearch cluster (6.X)
- with an index (your indexed data) having an identifier, a timestamp, a geometry and a centroid and registered in ARLAS-Exploration as a Collection with
ARLAS-server
- a running Kafka node
- a machine with Docker for running ARLAS-tagger.
docker run -d \
-p 9998:9998 \
-e "ARLAS_ELASTIC_CLUSTER=myelasticsearchclustername" \
-e "ARLAS_ELASTIC_NODES=elasticsearchhostname:9300" \
-e "KAFKA_BROKERS=kafkahostname:9092" \
gisaia/arlas-tagger:latest